Estimation of Rainfall Based on the Results of Polarimetric Echo Classification

2008 ◽  
Vol 47 (9) ◽  
pp. 2445-2462 ◽  
Author(s):  
Scott E. Giangrande ◽  
Alexander V. Ryzhkov
Keyword(s):  
Type A ◽  
New Method ◽  
Polarimetric Radar ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Different Types ◽  
Rms Error ◽  
Radar Resolution ◽  
Rainfall Estimates

Abstract The quality of polarimetric radar rainfall estimation is investigated for a broad range of distances from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D). The results of polarimetric echo classification have been integrated into the study to investigate the performance of radar rainfall estimation contingent on hydrometeor type. A new method for rainfall estimation that capitalizes on the results of polarimetric echo classification (EC method) is suggested. According to the EC method, polarimetric rainfall relations are utilized if the radar resolution volume is filled with rain (or rain and hail), and multiple R(Z) relations are used for different types of frozen hydrometeors. The intercept parameters in the R(Z) relations for each class are determined empirically from comparisons with gauges. It is shown that the EC method exhibits better performance than the conventional WSR-88D algorithm with a reduction by a factor of 1.5–2 in the rms error of 1-h rainfall estimates up to distances of 150 km from the radar.

scholarly journals Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach

2016 ◽  
Vol 9 (9) ◽  
pp. 4425-4445 ◽  
Author(s):  
Nikola Besic ◽  
Jordi Figueras i Ventura ◽  
Jacopo Grazioli ◽  
Marco Gabella ◽  
Urs Germann ◽  
...  
Keyword(s):  
Polarimetric Radar ◽  
Statistical Framework ◽  
X Band ◽  
Different Types ◽  
Unsupervised Approach ◽  
Radar Measurements ◽  
Supervised Classification Methods ◽  
Operational Potential ◽  
Statistical Clustering

Abstract. Polarimetric radar-based hydrometeor classification is the procedure of identifying different types of hydrometeors by exploiting polarimetric radar observations. The main drawback of the existing supervised classification methods, mostly based on fuzzy logic, is a significant dependency on a presumed electromagnetic behaviour of different hydrometeor types. Namely, the results of the classification largely rely upon the quality of scattering simulations. When it comes to the unsupervised approach, it lacks the constraints related to the hydrometeor microphysics. The idea of the proposed method is to compensate for these drawbacks by combining the two approaches in a way that microphysical hypotheses can, to a degree, adjust the content of the classes obtained statistically from the observations. This is done by means of an iterative approach, performed offline, which, in a statistical framework, examines clustered representative polarimetric observations by comparing them to the presumed polarimetric properties of each hydrometeor class. Aside from comparing, a routine alters the content of clusters by encouraging further statistical clustering in case of non-identification. By merging all identified clusters, the multi-dimensional polarimetric signatures of various hydrometeor types are obtained for each of the studied representative datasets, i.e. for each radar system of interest. These are depicted by sets of centroids which are then employed in operational labelling of different hydrometeors. The method has been applied on three C-band datasets, each acquired by different operational radar from the MeteoSwiss Rad4Alp network, as well as on two X-band datasets acquired by two research mobile radars. The results are discussed through a comparative analysis which includes a corresponding supervised and unsupervised approach, emphasising the operational potential of the proposed method.

scholarly journals Comparison of the Rossogolla (Balish) prepared in the laboratory and collected from market of Netrokona District

1970 ◽  
Vol 39 (1-2) ◽  
pp. 134-143
Author(s):  
MF Rahman ◽  
MN Islam ◽  
MN Hasan ◽  
MSR Siddiki ◽  
F Naznin
Keyword(s):  
Chemical Analysis ◽  
Key Words ◽  
Type A ◽  
Local Market ◽  
Total Solids ◽  
Physical Chemical ◽  
Different Types ◽  
Physical Examinations ◽  
Microbial Status

This experiment assessed the quality of Rossogolla (Balish) manufactured in the laboratory and collected from Netrokona district. Four types of Rossogolla, (A) Laboratory made Rossogolla, (B) Rossogolla from Mukti Mistanno vander, (C) Rossogolla from Gwyneth Mistanno vander, (D) Rossogolla from Khan Mistanno vander were used in this experiment. Quality of Rossogolla were evaluated by physical, chemical and microbiological tests. From the physical examinations it appears that quality differed among different Rossogolla. Significant differences within flavour score, body and texture, colour and appearance and taste among the four different preparations of Rossogolla (P<0.05) were seen. The chemical analysis score implies that Total solids, Protein, Fat, Carbohydrate, Ash and microbial status varied significantly among four different types of Rossogolla of Netrokona district. Overall Physical, Chemical and Microbial status indicate that the type (A) Laboratory made Rossogolla was superior in comparison with others. Key words: Rossogolla; Laboratory; Local market DOI: http://dx.doi.org/10.3329/bjas.v39i1-2.9686 Bang. J. Anim. Sci. 2010, 39(1&2): 134-143

scholarly journals Rainfall Estimates with Respect to Rainfall Types Using S-Band Polarimetric Radar in Korea

Atmosphere ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 773
Author(s):  
Cheolhwan You ◽  
Miyoung Kang ◽  
Dong-In Lee
Keyword(s):  
Polarimetric Radar ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Rainfall Events ◽  
Stratiform Rain ◽  
Rainfall Variation ◽  
Precipitation Type ◽  
Rainfall Type ◽  
The Impact

To investigate the impact of rainfall type on rainfall estimation using polarimetric variables, rainfall relations such as those between rain rate (R) and specific differential phase (KDP), between R and KDP/differential reflectivity (ZDR), and between R and reflectivity (Z)/ZDR, were examined with respect to the precipitation type classified using drop size distributions (DSDs) measured by a disdrometer. The classification of rainfall type was assessed using four different methods: temporal rainfall variation; and the relations between intercept parameter (N0) and R; normalized intercept parameter (Nw) and median diameter (D0); and slope parameter (Λ) and R. The logN0–R relation discriminated between convective and stratiform rain with less standard deviation than the other methods as shown by the Z–ZDR scatter with respect to the rainfall types. The transition type from convective to stratiform and vice versa occurred in the stratiform rain region for all methods. To apply the classified rainfall relations to radar rainfall estimation, logNw and D0 were retrieved from polarimetric variables to discriminate the rainfall types in the radar domain. The DSD classification was verified with the vertical profile of reflectivity extracted at two positions corresponding to gage sites. Statistical analysis of four different rainfall events showed that rainfall estimation using the relations with precipitation type were better than those obtained without classification. The R(KDP,ZDR) relation with classification performed best on rainfall estimation for all rainfall events. The greatest improvement in rainfall estimation was obtained from R(Z,ZDR) with classification. We conclude that the classification of rainfall type leads to more accurate rainfall estimation. The different relations R(KDP), R(KDP,ZDR), and R(Z,ZDR) with respect to the rain types using polarimetric radar show improvement compared to estimation without consideration of rainfall type, in Korea.

scholarly journals Influence of Beam Broadening on the Accuracy of Radar Polarimetric Rainfall Estimation

2015 ◽  
Vol 16 (3) ◽  
pp. 1356-1371 ◽  
Author(s):  
Eugenio Gorgucci ◽  
Luca Baldini
Keyword(s):  
Quantitative Estimation ◽  
Point Of View ◽  
Major Theme ◽  
Polarimetric Radar ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Large Variability ◽  
Radar Measurements ◽  
Rain Radar ◽  
Beam Broadening

Abstract The quantitative estimation of rain rates using meteorological radar has been a major theme in radar meteorology and radar hydrology. The increase of interest in polarimetric radar is in part because polarization diversity can reduce the effect on radar precipitation estimates caused by raindrop size variability, which has allowed progress on radar rainfall estimation and on hydrometeorological applications. From an operational point of view, the promises regarding the improvement of radar rainfall accuracy have not yet been completely proven. The main reason behind these limits is the geometry of radar measurements combined with the variability of the spatial structure of the precipitation systems. To overcome these difficulties, a methodology has been developed to transform the estimated drop size distribution (DSD) provided by a vertically pointing micro rain radar to a profile given by a ground-based polarimetric radar. As a result, the rainfall rate at the ground is fixed at all ranges, whereas the broadening beam encompasses a large variability of DSDs. The resulting DSD profile is used to simulate the corresponding profile of radar measurements at C band. Rainfall algorithms based on polarimetric radar measurements were taken into account to estimate the rainfall into the radar beam. Finally, merit factors were used to achieve a quantitative analysis of the performance of the rainfall algorithm in comparison with the corresponding measurements at the ground obtained from a 2D video disdrometer (2DVD) that was positioned beside the micro rain radar. In this method, the behavior change of the merit factors in the range is directly attributable to the DSD variability inside the radar measurement volume, thus providing an assessment of the effects due to beam broadening.

scholarly journals Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach

2016 ◽  
Author(s):  
Nikola Besic ◽  
Jordi Figueras i Ventura ◽  
Jacopo Grazioli ◽  
Marco Gabella ◽  
Urs Germann ◽  
...  
Keyword(s):  
Polarimetric Radar ◽  
Statistical Framework ◽  
X Band ◽  
Different Types ◽  
Unsupervised Approach ◽  
Radar Measurements ◽  
Electromagnetic Behavior ◽  
Supervised Classification Methods ◽  
Statistical Clustering

Abstract. Hydrometeor classification is the procedure of identifying different types of hydrometeors by exploiting polarimetric radar observations. The main drawback of the existing supervised classification methods, mostly based on fuzzy logic, is a significant dependency on a presumed electromagnetic behavior of different hydrometeor types. Namely, the results of classification largely rely upon the quality of scattering simulations. When it comes to the unsupervised approach, it eventually lacks the constraints related to the hydrometeor microphysics. The idea of the proposed method is to compensate for these drawbacks by combining the two approaches in a way that microphysical hypotheses can, to a degree, adjust the content of the classes obtained statistically from the observations. This is done by means of an iterative approach which, in a statistical framework, examines clustered representative polarimetric observations by comparing them to the presumed polarimetric properties of hydrometeor classes. Aside from comparing, a routine alters the content of clusters by encouraging further statistical clustering in case of non-identification. By merging all identified clusters, the multi-dimensional polarimetric signatures of various hydrometeor types are obtained for each of the studied representative datasets, i.e. for each radar system of interest. These are depicted by sets of centroids which are finally employed in operational labeling of different hydrometeors. The method has been applied on three C-band datasets, each acquired by different operational radar from the MeteoSwiss Rad4Alp network, as well as on an X-band dataset acquired by a research radar. The results are discussed through a comparative analysis which includes a corresponding supervised and unsupervised approach, with a particular emphasis on hail detection performances.

Modeling probabilistic radar rainfall estimation at ungauged locations based on spatiotemporal errors which correspond to gauged data

2013 ◽  
Vol 46 (1) ◽  
pp. 39-59 ◽  
Author(s):  
Shiang-Jen Wu ◽  
Ho-Cheng Lien ◽  
Chih-Tsung Hsu ◽  
Che-Hao Chang ◽  
Jhih-Cyuan Shen
Keyword(s):  
Model Development ◽  
Temporal Variations ◽  
Cumulative Probability ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Multiple Sensors ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Reservoir Watershed ◽  
Rainfall Estimates

This study presents a probabilistic radar rainfall estimation (PRRE) model to quantify the reliability and accuracy of the resulting radar rainfall estimates at ungauged locations from a radar-based quantitative precipitation estimation (QPE) model. This model primarily estimates the quantiles of the radar rainfall errors at ungauged locations by incorporating seven spatiotemporal variogram models with a nonparametric sample quantile estimate method based on the radar rainfall errors at rain gauges. Then, by adding the resulting error quantiles to the radar rainfall estimates, the corresponding radar rainfall quantiles can be obtained. The QPE system Quantitative Precipitation Estimation Using Multiple Sensors (QPESUMS) provides hourly observed and radar precipitation for three typhoons in the Shinmen reservoir watershed in Northern Taiwan, which are used in the model development and validation. The results indicate that the proposed PRRE model can quantify the spatial and temporal variations of radar rainfall estimates at ungauged locations provided by the QPESUMS system. Also, its reliability and accuracy could be evaluated based on a 95% confidence interval and occurrence probability resulting from the cumulative probability distribution established by the proposed PRRE model.

UGR CALCULATION BASED ON THE LUMINANCE SPATIAL-ANGULAR DISTRIBUTION

2020 ◽  
Vol 2020 (4) ◽  
pp. 25-32
Author(s):  
Viktor Zheltov ◽  
Viktor Chembaev
Keyword(s):  
Monte Carlo ◽  
Angular Distribution ◽  
Monte Carlo Method ◽  
Visual Task ◽  
New Method ◽  
Lighting System ◽  
Preliminary Assessment ◽  
System A ◽  
The Monte Carlo Method

The article has considered the calculation of the unified glare rating (UGR) based on the luminance spatial-angular distribution (LSAD). The method of local estimations of the Monte Carlo method is proposed as a method for modeling LSAD. On the basis of LSAD, it becomes possible to evaluate the quality of lighting by many criteria, including the generally accepted UGR. UGR allows preliminary assessment of the level of comfort for performing a visual task in a lighting system. A new method of "pixel-by-pixel" calculation of UGR based on LSAD is proposed.

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